{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils._bunch.Bunch"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.4, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.3, 0.2],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.7, 0.4],\n",
       "       [1.4, 0.3],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.5, 0.1],\n",
       "       [1.5, 0.2],\n",
       "       [1.6, 0.2],\n",
       "       [1.4, 0.1],\n",
       "       [1.1, 0.1],\n",
       "       [1.2, 0.2],\n",
       "       [1.5, 0.4],\n",
       "       [1.3, 0.4],\n",
       "       [1.4, 0.3],\n",
       "       [1.7, 0.3],\n",
       "       [1.5, 0.3],\n",
       "       [1.7, 0.2],\n",
       "       [1.5, 0.4],\n",
       "       [1. , 0.2],\n",
       "       [1.7, 0.5],\n",
       "       [1.9, 0.2],\n",
       "       [1.6, 0.2],\n",
       "       [1.6, 0.4],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.6, 0.2],\n",
       "       [1.6, 0.2],\n",
       "       [1.5, 0.4],\n",
       "       [1.5, 0.1],\n",
       "       [1.4, 0.2],\n",
       "       [1.5, 0.2],\n",
       "       [1.2, 0.2],\n",
       "       [1.3, 0.2],\n",
       "       [1.4, 0.1],\n",
       "       [1.3, 0.2],\n",
       "       [1.5, 0.2],\n",
       "       [1.3, 0.3],\n",
       "       [1.3, 0.3],\n",
       "       [1.3, 0.2],\n",
       "       [1.6, 0.6],\n",
       "       [1.9, 0.4],\n",
       "       [1.4, 0.3],\n",
       "       [1.6, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [1.5, 0.2],\n",
       "       [1.4, 0.2],\n",
       "       [4.7, 1.4],\n",
       "       [4.5, 1.5],\n",
       "       [4.9, 1.5],\n",
       "       [4. , 1.3],\n",
       "       [4.6, 1.5],\n",
       "       [4.5, 1.3],\n",
       "       [4.7, 1.6],\n",
       "       [3.3, 1. ],\n",
       "       [4.6, 1.3],\n",
       "       [3.9, 1.4],\n",
       "       [3.5, 1. ],\n",
       "       [4.2, 1.5],\n",
       "       [4. , 1. ],\n",
       "       [4.7, 1.4],\n",
       "       [3.6, 1.3],\n",
       "       [4.4, 1.4],\n",
       "       [4.5, 1.5],\n",
       "       [4.1, 1. ],\n",
       "       [4.5, 1.5],\n",
       "       [3.9, 1.1],\n",
       "       [4.8, 1.8],\n",
       "       [4. , 1.3],\n",
       "       [4.9, 1.5],\n",
       "       [4.7, 1.2],\n",
       "       [4.3, 1.3],\n",
       "       [4.4, 1.4],\n",
       "       [4.8, 1.4],\n",
       "       [5. , 1.7],\n",
       "       [4.5, 1.5],\n",
       "       [3.5, 1. ],\n",
       "       [3.8, 1.1],\n",
       "       [3.7, 1. ],\n",
       "       [3.9, 1.2],\n",
       "       [5.1, 1.6],\n",
       "       [4.5, 1.5],\n",
       "       [4.5, 1.6],\n",
       "       [4.7, 1.5],\n",
       "       [4.4, 1.3],\n",
       "       [4.1, 1.3],\n",
       "       [4. , 1.3],\n",
       "       [4.4, 1.2],\n",
       "       [4.6, 1.4],\n",
       "       [4. , 1.2],\n",
       "       [3.3, 1. ],\n",
       "       [4.2, 1.3],\n",
       "       [4.2, 1.2],\n",
       "       [4.2, 1.3],\n",
       "       [4.3, 1.3],\n",
       "       [3. , 1.1],\n",
       "       [4.1, 1.3],\n",
       "       [6. , 2.5],\n",
       "       [5.1, 1.9],\n",
       "       [5.9, 2.1],\n",
       "       [5.6, 1.8],\n",
       "       [5.8, 2.2],\n",
       "       [6.6, 2.1],\n",
       "       [4.5, 1.7],\n",
       "       [6.3, 1.8],\n",
       "       [5.8, 1.8],\n",
       "       [6.1, 2.5],\n",
       "       [5.1, 2. ],\n",
       "       [5.3, 1.9],\n",
       "       [5.5, 2.1],\n",
       "       [5. , 2. ],\n",
       "       [5.1, 2.4],\n",
       "       [5.3, 2.3],\n",
       "       [5.5, 1.8],\n",
       "       [6.7, 2.2],\n",
       "       [6.9, 2.3],\n",
       "       [5. , 1.5],\n",
       "       [5.7, 2.3],\n",
       "       [4.9, 2. ],\n",
       "       [6.7, 2. ],\n",
       "       [4.9, 1.8],\n",
       "       [5.7, 2.1],\n",
       "       [6. , 1.8],\n",
       "       [4.8, 1.8],\n",
       "       [4.9, 1.8],\n",
       "       [5.6, 2.1],\n",
       "       [5.8, 1.6],\n",
       "       [6.1, 1.9],\n",
       "       [6.4, 2. ],\n",
       "       [5.6, 2.2],\n",
       "       [5.1, 1.5],\n",
       "       [5.6, 1.4],\n",
       "       [6.1, 2.3],\n",
       "       [5.6, 2.4],\n",
       "       [5.5, 1.8],\n",
       "       [4.8, 1.8],\n",
       "       [5.4, 2.1],\n",
       "       [5.6, 2.4],\n",
       "       [5.1, 2.3],\n",
       "       [5.1, 1.9],\n",
       "       [5.9, 2.3],\n",
       "       [5.7, 2.5],\n",
       "       [5.2, 2.3],\n",
       "       [5. , 1.9],\n",
       "       [5.2, 2. ],\n",
       "       [5.4, 2.3],\n",
       "       [5.1, 1.8]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(tree_clf, out_file='iris_tree.dot', feature_names=iris.feature_names[2:], class_names=iris.target_names, rounded=True, filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sepal length (cm)',\n",
       " 'sepal width (cm)',\n",
       " 'petal length (cm)',\n",
       " 'petal width (cm)']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3.8.5 ('ecg_biomarkers': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
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  "vscode": {
   "interpreter": {
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 },
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